Advancements of Football Data Analysis Based on Machine Learning Algorithms

: Football is the most influential sport in the world and has a significant impact on the global economy. With the development of big data technology, the application of artificial intelligence in the statistical analysis of football data has become increasingly widespread. This article summarizes relevant research on machine learning in football, with a special focus on prediction methods based on football match data. These methods include hybrid learning models, binary classification and regression, TOPSIS methods, as well as expert systems and ensemble learning. By iterative training on historical match data, an estimate of the ability of each team in the training data can be obtained. A regression-based model is used to calculate the number of goals scored by each team. Sensitivity analysis can be used to assess the impact of different weighting schemes or criteria choices on player rankings. Principal Component Analysis (PCA) can help identify underlying patterns and relationships in player and team performance metrics. Support Vector Machine (SVM) classifiers can effectively learn decision boundaries based on relevant features. These models predict various outcomes by analyzing historical match data and player performance with some degree of success. However, the subjective nature of football matches and refereeing decisions can lead to inaccurate model predictions, emphasizing the importance of explaining model decisions. To improve the applicability of the model, it is suggested that the AI model be applied to different levels of football leagues. Real-time data analytics tools such as Apache Spark can be used to address the challenges of real-time data and processing diversity.